why is it better make dependent variables continuous

Making dependent variables continuous can have several advantages in statistical analysis:

1. More Information: Continuous variables can provide more information because they can take on an infinite number of values. For example, a variable like age can be more informative if it's treated as a continuous variable (e.g., 22.3 years old) rather than a categorical one (e.g., "20-30 years old").

2. More Statistical Power: Continuous variables can provide more statistical power. This is because they can utilize a wider range of statistical tests. Many statistical tests, like t-tests or ANOVA, require the dependent variable to be continuous.

3. More Accurate Models: Continuous variables can lead to more accurate predictive models. This is because they can capture more nuanced relationships between variables. For example, a linear regression model can capture the fact that each additional year of age might increase the risk of a certain disease by a certain percentage.

4. Avoiding Arbitrary Categories: By making dependent variables continuous, you can avoid the need to create arbitrary categories. This can prevent loss of information and potential bias.

5. Better Understanding of Relationships: Continuous variables can help you better understand the relationships between variables. For example, you might be able to see a clear trend or pattern when you plot a continuous variable, which might not be as clear if the variable was categorical.

Remember, while continuous variables can have these advantages, it's also important to consider the nature of your data and research question. In some cases, treating a variable as categorical might be more appropriate.